xlm-roberta-ner-japanese

(Japanese caption : 日本語の固有表現抽出のモデル)

This model is a fine-tuned version of xlm-roberta-base (pre-trained cross-lingual RobertaModel) trained for named entity recognition (NER) token classification.

The model is fine-tuned on NER dataset provided by Stockmark Inc, in which data is collected from Japanese Wikipedia articles.
See here for the license of this dataset.

Each token is labeled by :

Intended uses

from transformers import pipeline

model_name = "tsmatz/xlm-roberta-ner-japanese"
classifier = pipeline("token-classification", model=model_name)
result = classifier("鈴井は4月の陽気の良い日に、鈴をつけて北海道のトムラウシへと登った")
print(result)

Training procedure

You can download the source code for fine-tuning from here.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 12
• eval_batch_size: 12
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• Transformers 4.23.1
• Pytorch 1.12.1+cu102
• Datasets 2.6.1
• Tokenizers 0.13.1